A Microscopic Prediction Model for Traffic Noise in Adjacent Regions to Arterial Roads by Ming LI, Jizhou LIU

“…Based on the validated method, the spatial distribution of traffic noise near the arterial road is investigated for different traffic scenarios.…”
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Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning by FENG Xin, LIU Yanju, TONG Hongfu, QIAN Shuni

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When Time Prevails: The Perils of Overlooking Temporal Landscape Evolution in Landslide Susceptibility Predictions by Jinping Liu, Panxing He, Jianhua Xiao, Qingfeng Hu, Yanqun Ren, Aiding Kornejady, Huiran Gao

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Research on water quality prediction of Jiangshan Port based on SCV-CBA model by Yiting Xu, Zhaoju Liu

“…CNN was employed to capture spatial patterns in the data, while BiLSTM network modelled temporal dependencies. …”
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19 Predicting daily PM2.5 in Mexico City: A hybrid spatiotemporal modeling approach by Mike He, Ellen Ren, Iván Gutiérrez-Avila, Itai Kloog

“…Objectives/Goals: In recent years, there has been growing interest in the development of air pollution prediction models, particularly in low- and middle-income countries that are disproportionately impacted by the effects of air pollution. …”
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Predicting transient response using data-driven models for ball-impact simulations by Ross Pivovar, Fei Chen, Raghunath Katragadda, Vidyasagar Ananthan

“…This study investigates the application of machine learning (ML) models for predicting transient responses in ball-impact elastodynamics simulations. …”
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Research on Stock Index Prediction Based on the Spatiotemporal Attention BiLSTM Model by Shengdong Mu, Boyu Liu, Jijian Gu, Chaolung Lien, Nedjah Nadia

“…Stock index fluctuations are characterized by high noise and their accurate prediction is extremely challenging. To address this challenge, this study proposes a spatial–temporal–bidirectional long short-term memory (STBL) model, incorporating spatiotemporal attention mechanisms. …”
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Spatio-Temporal Graph Neural Networks for Streamflow Prediction in the Upper Colorado Basin by Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

“…This study presents a spatio-temporal graph neural network (STGNN) model for streamflow prediction in the Upper Colorado River Basin (UCRB), integrating graph convolutional networks (GCNs) to model spatial connectivity and long short-term memory (LSTM) networks to capture temporal dynamics. …”
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Spatial and temporal evolution of carbon stocks in Yulin City under changing environments by Guikai Sun, Yadong Li, Rui Huang, Chongxun Mo

“…It then applies the PLUS model to predict the land use of Yulin City under different scenarios in 2030 and forecasts the future carbon stock, providing a theoretical basis for the city’s future development planning. …”
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IGWO-MSVR model for predicting stress in coal seam during drilling process by Jian Tan, Yanfeng Geng, Liangke Xu

“…Furthermore, Back Propagation Neural network model (BP), Spatial Autoregressive model (SAR) and MSVR model were adopted to perform the stress prediction, and the stress prediction accuracy from these models was analyzed. …”
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Automated Cardiac Disease Prediction Using Composite GAN and DeepLab Model by Sohail Jabbar, Umar Raza, Muhammad Asif Habib, Muhammad Farhan, Saqib Saeed

“…Meanwhile, the DeepLab segmentation network exploits inter-slice spatial contexts for precise anatomical quantification. …”
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A Comprehensive Deep Learning System With MGRF Modeling for Predicting Breast Cancer Response to Neoadjuvant Chemotherapy by Ahmed Sharafeldeen, Fatma Taher, Norah Saleh Alghamdi, Eman Alnaghy, Reham Alghandour, Khadiga M. Ali, Sameh Shamaa, Abdelrahman Gamal, Mohammed Ghazal, Sohail Contractor, Ayman El-Baz

“…First, tumor regions are delineated across MRI modalities and then modeled using a translation-invariant Markov-Gibbs random field (MGRF) with analytical parameter estimation to capture modality-specific spatial appearance patterns correlated with NAC response. …”
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On the development of a systemic (biopsychosocial) prediction model for cardiovascular disease. Part I by O. Yu. Shchelkova, M. V. Iakovleva, D. A. Eremina, R. Yu. Shindrikov, N. E. Kruglova, I. A. Gorbunov, E. A. Demchenko

“…The study included 437  patients with coronary heart disease and/or chronic heart failure undergoing surgical treatment.Part  I of the article presents the results of the first 4 stages of the study. 1) A theoretical prediction model based on existing data was developed and empirically tested on different patient populations at various stages of surgical treatment. 2) An overall information database was compiled on the basis of our own research. …”
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Multi-scale machine learning model predicts muscle and functional disease progression by Silvia S. Blemker, Lara Riem, Olivia DuCharme, Megan Pinette, Kathryn Eve Costanzo, Emma Weatherley, Jeff Statland, Stephen J. Tapscott, Leo H. Wang, Dennis W. W. Shaw, Xing Song, Doris Leung, Seth D. Friedman

“…All model stages revealed strong predictive performance in separate holdout datasets. …”
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PM2.5 prediction and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration using spatial temporal graph convolutional networks by Yawen Zhao

“…To address this, this study uses spatiotemporal analysis and Spatial Temporal Graph Convolutional Networks (ST-GCN) to evaluate the variation and driving factors of PM _2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) urban agglomeration from 2014 to 2024, and to make predictions. …”
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Determination of Spatial-Temporal Correlation Structure of Troposphere Ozone Data in Tehran City by S.S. Mousavi, M. Mohammadzadeh

“…Spatial-temporal modeling of air pollutants, ground-level ozone concentrations in particular, has attracted recent attention because by using spatial-temporal modeling, can analyze, interpolate or predict ozone levels at any location. …”
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Soil Moisture Content Prediction Using Gradient Boosting Regressor (GBR) Model: Soil-Specific Modeling with Five Depths by Tarek Alahmad, Miklós Neményi, Anikó Nyéki

“…Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. …”
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A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin by Hugo J. M. Miniere, Ernesto A. B. F. Lima, Guillermo Lorenzo, David A. Hormuth II, Sophia Ty, Amy Brock, Thomas E. Yankeelov

“…To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tumor heterogeneities. Biology-inspired mathematical models are capable of attacking this problem, but tumor heterogeneity is often overlooked in in-vivo modeling studies, while phenotypic considerations capturing spatial dynamics are not typically included in in-vitro modeling studies. …”
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Simulation and Prediction of Spring Snow Cover in Northern Hemisphere by CMIP6 Model by Xulei WANG, Hui SUN, Hui GUO, Chula SA, Fanhao MENG, Min LUO

“…As one of the most sensitive natural elements in response to climate change， snow cover has a significant effect on the Earth's surface radiation balance and water cycle.The global snow cover area is approximately 46×106 km2 and 98% of the snow cover distributed in the Northern Hemisphere.Due to its distinctive radiative properties （high surface albedo） and thermal characteristics （low thermal conductivity）， changes in snow cover play a crucial role in the energy balance and water cycle between land and the atmosphere.In the context of global warming， the snow cover in the Northern Hemisphere has been decreasing in recent decades， especially in the spring.Therefore， the capabilities of CMIP6 （Coupled Model Intercomparison Project Phase 6） data to simulate the snow cover area were evaluated based on observational data and the future changes in snow cover were also assessed using a multi-model average in this study.By using the snow cover products from the National Oceanic and Atmospheric Administration/National Climatic Data Center （NOAA/NCDC） as reference data， the Taylor skill scoring， relative deviation， and other methods were applied to evaluate the spring snow cover （SCF） data in the Northern Hemisphere from the International Coupled Model Comparison Project Phase 6 （CMIP6） during 1982 -2014.The ensemble average of the top three models was further selected to predict the spatiotemporal variation characteristics of SCF under different emission scenarios from 2015 to 2099， providing insights into the modeling capabilities of CMIP6 and future changes in SCF.During the historical period （1982 -2014）， SCF was characterized by high coverage at high latitudes and low coverage at low latitudes， with high-altitude regions such as Tibetan Plateau and eastern Asia having higher snow coverage than those at the same latitudes.Overall， 68.37% of the regions in the Northern Hemisphere showed a decreasing trend in SCF， while 31.63% of the regions showed an increasing trend in SCF.Most CMIP6 models overestimated SCF in the Tibetan Plateau region compared to the reference data.In addition， most models simulated larger areas with a decreasing trend in SCF than those evaluated by the reference data and underestimated SCF in March， April， and May.Various models exhibited differing abilities to simulate SCF， with NorESM2-MM， CESM2， BBC-CSM2-MR， NorESM2-LM， and CESM2-WACCM demonstrating superior capabilities.The Multi-Model Ensemble Mean （MME） consistently outperformed individual models， closely aligning with observational data.There were significant differences in the ability of the CMIP6 models to simulate the spatial distribution， inter-annual variation trends， and intra-annual variations of SCF in the Northern Hemisphere.At the end of the 21st-century （2067 -2099）， SCF in the Northern Hemisphere exhibited a decreasing trend in most areas， which intensifies with increasing emission intensity.The changes in SCF were relatively consistent under different emission scenarios before 2040.SCF maintains a steady state under the SSP1-2.6 scenario， showed a slight decreasing trend under the SSP2-4.5 scenario， and showed a significant decreasing trend under the SSP5-8.5 scenario after 2040.…”
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Comparision of Kernel Functions in Geographically Weighted Regression Model: Suicide Data as an Application by Tuba Koç, Pelin Akın

“…In this study, the number of suicides data was used for Turkey’s 81 provinces in 2019.The effects of factors affecting suicide and spatial differences on suicide were analyzed and predicted with geographically weighted regression models (GWR). …”
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